The Experts below are selected from a list of 183 Experts worldwide ranked by ideXlab platform

Frederick E Petry - One of the best experts on this subject based on the ideXlab platform.

  • extension of the Relational Database and its algebra with rough set techniques
    Computational Intelligence, 1995
    Co-Authors: Theresa Beaubouef, Frederick E Petry, Bill P. Buckles
    Abstract:

    This paper describes a Database Model based on the original rough sets theory. Its rough relations permit the representation of a rough set of tuples not definable in terms of the elementary classes, except through use of lower and upper approximations. The rough Relational Database Model also incorporates indiscernibility in the representation and in all the operators of the rough Relational algebra. This indiscernibility is based strictly on equivalence classes which must be defined for every attribute domain. There are several obvious applications for which the rough Relational Database Model can more accurately Model an enterprise than does the standard Relational Model. These include systems involving ambiguous, imprecise, or uncertain data. Retrieval over mismatched domains caused by the merging of one or more applications can be facilitated by the use of indiscernibility, and naive system users can achieve greater recall with the rough Relational Database. In addition, applications inherently rough could be more easily implemented and maintained in the rough Relational Database.

  • fuzzy set quantification of roughness in a rough Relational Database Model
    World Congress on Computational Intelligence, 1994
    Co-Authors: T Beaubouef, Frederick E Petry
    Abstract:

    This paper provides the theoretical background and important definitions for rough sets. It defines fuzzy rough sets and provides some basic properties of union and intersection of fuzzy rough sets. It then informally presents the basic properties of the rough Relational Database Model. Because the fuzzy rough Relational Database (FRRD) is an extension of the basic rough Relational Database Model, only a short, informal description of the basic Model is provided. The FRRD Model is described providing necessary definitions and discussion of operators. Advantages of the enhanced Model are discussed. >

Bill P. Buckles - One of the best experts on this subject based on the ideXlab platform.

  • Uncertainty in a nested Relational Database Model
    Data and Knowledge Engineering, 1999
    Co-Authors: Adnan Yazici, Alper Soysal, Bill P. Buckles, Fred E. Petry
    Abstract:

    Some Database Models have already been developed to deal with complex values but they have constrains that data stored is precise and queries are crisp. However, as many researchers have pointed out, there is a need to present, manipulate, and query complex and uncertain data of various non-traditional Database applications such as oceanography, multimedia, meteorology, office automation systems, engineering designs, expert Database systems and geographic information systems. In this paper, we present a logical Database Model, which is an extension of a nested Relational data Model (also known as an NF2data Model), for representing and manipulating complex and uncertain data in Databases. We also introduce a possible physical representation of such complex and uncertain values in Databases and describe the query processing of the Model that we discuss here.

  • extension of the Relational Database and its algebra with rough set techniques
    Computational Intelligence, 1995
    Co-Authors: Theresa Beaubouef, Frederick E Petry, Bill P. Buckles
    Abstract:

    This paper describes a Database Model based on the original rough sets theory. Its rough relations permit the representation of a rough set of tuples not definable in terms of the elementary classes, except through use of lower and upper approximations. The rough Relational Database Model also incorporates indiscernibility in the representation and in all the operators of the rough Relational algebra. This indiscernibility is based strictly on equivalence classes which must be defined for every attribute domain. There are several obvious applications for which the rough Relational Database Model can more accurately Model an enterprise than does the standard Relational Model. These include systems involving ambiguous, imprecise, or uncertain data. Retrieval over mismatched domains caused by the merging of one or more applications can be facilitated by the use of indiscernibility, and naive system users can achieve greater recall with the rough Relational Database. In addition, applications inherently rough could be more easily implemented and maintained in the rough Relational Database.

O. Pons - One of the best experts on this subject based on the ideXlab platform.

  • a fuzzy temporal object Relational Database Model and implementation
    Journal of intelligent systems, 2014
    Co-Authors: J M Medina, Enrique J Pons, Carlos D Barranco, O. Pons
    Abstract:

    In real world, some data have a specific temporal validity that must be appropiately managed. To deal with this kind of data, several proposals of temporal Databases have been introduced. Moreover, time can also be affected by imprecision, vagueness, and/or uncertainty, since human beings manage time using temporal indications and temporal notions, which may also be imprecise. For this reason, information systems require appropriate support to accomplish this task. In this work, we present a novel possibilistic valid time Model for fuzzy Databases including the data structures, the integrity constraints, and the DML. Together with this Model, we also present its implementation by means of a fuzzy valid time support module on top of a fuzzy object-Relational Database system. The integration of these modules allows to perform queries that combines fuzzy valid time constraints together with fuzzy predicates. Besides, the Model and implementation proposed support the crisp valid time Model as a particular case of the fuzzy valid time support provided.

  • Towards the implementation of a generalized fuzzy Relational Database Model
    Fuzzy Sets and Systems, 1995
    Co-Authors: J M Medina, M. A. Vila, Juan-carlos Cubero, O. Pons
    Abstract:

    This paper shows the necessary elements for the effective implementation of the generalized fuzzy Relational Database Model. From the Model described in Medina et al. (1994) some criteria for representation and handling of imprecise information are introduced, the most important aspect being the simplicity of the implementation. The paper shows a series of mechanisms to implement imprecise information in a classical RDBMS. Having the information represented in a classical RDBMS data structure and having the implementation of procedural knowledge about such information, we will be able to build a FRDBMS on a host RDBMS. © 1995.

Ashraf Labib - One of the best experts on this subject based on the ideXlab platform.

  • enhanced fuzzy object Relational Database Model for efficient implementation of the fsm
    IEEE International Conference on Fuzzy Systems, 2015
    Co-Authors: Sabrine Jandoubi, Afef Bahri, Salem Chakhar, Nadia Yacoubiayadi, Ashraf Labib
    Abstract:

    The objective of this paper is to present the first results concerning the mapping and implementation of the Fuzzy Semantic Model (FSM) as a Fuzzy Object-Relational Database Model (FuzzORM). This solution permits to take advantages of both Relational and object-oriented Databases. The object-Relational Database management system PostgreSQL has been used for the implementation of the FuzzORM.

Sabrine Jandoubi - One of the best experts on this subject based on the ideXlab platform.

  • enhanced fuzzy object Relational Database Model for efficient implementation of the fsm
    IEEE International Conference on Fuzzy Systems, 2015
    Co-Authors: Sabrine Jandoubi, Afef Bahri, Salem Chakhar, Nadia Yacoubiayadi, Ashraf Labib
    Abstract:

    The objective of this paper is to present the first results concerning the mapping and implementation of the Fuzzy Semantic Model (FSM) as a Fuzzy Object-Relational Database Model (FuzzORM). This solution permits to take advantages of both Relational and object-oriented Databases. The object-Relational Database management system PostgreSQL has been used for the implementation of the FuzzORM.

  • Mapping the Fuzzy Semantic Model into Fuzzy Object Relational Database Model
    2015
    Co-Authors: Sabrine Jandoubi, Afef Bahri, Salem Chakhar, Nadia Yacoubi-ayadi
    Abstract:

    This paper discusses the mapping of the Fuzzy Se- mantic Model (FSM) into a Fuzzy Object Relational Database Model (FuzzORM). We designed a set of mapping rules to transform all the constructs of the FSM into the FuzzORM. A prototype supporting these rules is under development over the Object-Relational Database Management System (ORDBMS) PostgreSQL. The first results of implementation are presented in this paper. Keywords-Fuzzy Database; Imperfect information; Mapping rule; Object Relational Database; Semantic Modeling.